{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "3.2.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yananma/5_programs_per_day/blob/master/pytorch/ch3/3_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yT23NKPGZYHJ",
        "colab_type": "text"
      },
      "source": [
        "## 3.2 线性回归的从零开始实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVZyKUgmaCzJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import torch \n",
        "from IPython import display \n",
        "from matplotlib import pyplot as plt \n",
        "import numpy as np \n",
        "import random "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9K8yLx97r2gu",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.1 生成数据集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjaipSf-r9zq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_inputs = 2     # 2 个特征\n",
        "num_examples = 1000     # 1000 个样本\n",
        "true_w = [2, -3.4]\n",
        "true_b = 4.2 \n",
        "features = torch.randn(num_examples, num_inputs, dtype=torch.float32)\n",
        "labels = true_w[0] * features[:, 0] + true_w[1] * features[:, 1] + true_b \n",
        "labels += torch.tensor(np.random.normal(0, 0.01, size=labels.size()), dtype=torch.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wW836bWXt8w0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5e18a034-1118-4900-c06f-a830b25caddf"
      },
      "source": [
        "features[0], labels[0]"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([ 0.1620, -0.6597]), tensor(6.7839))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NQSF6m1SuHaz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8a0a91b3-b8a2-44bf-d2c2-6788afb8fe12"
      },
      "source": [
        "features.shape    # 1000 层，2 列"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP7dw4CruTiV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "33ea8cec-d1d4-4b0e-993d-fa7263e47b76"
      },
      "source": [
        "labels.shape    # 1000 层"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1000])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqExwK1AuVYj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "0d172837-af4f-47a7-e04e-d1b3aa4ce5a2"
      },
      "source": [
        "def use_svg_display():\n",
        "    # 矢量图显示\n",
        "    display.set_matplotlib_formats('svg')\n",
        "\n",
        "def set_figsize(figsize=(3.5, 2.5)):\n",
        "    use_svg_display()\n",
        "    plt.rcParams['figure.figsize'] = figsize \n",
        "\n",
        "set_figsize()\n",
        "plt.scatter(features[:, 1].numpy(), labels.numpy(), 1);    # 第二个特征 1000 个数，和 label 画图"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 252x180 with 1 Axes>"
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppq18fuuwLBa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "047e8082-0c6f-4838-c704-38bff9f16329"
      },
      "source": [
        "len(features)"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ioGe2lEQ7A52",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.2 读取数据集"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iLHeoSAv0eP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 这个函数每次返回 batch_size 个随机样本的特征和标签\n",
        "def data_iter(batch_size, features, labels):\n",
        "    num_examples = len(features)    # num_examples 等于 1000，是一个数\n",
        "    indices = list(range(num_examples))    # 列表，有 1000 个元素\n",
        "    random.shuffle(indices)    # 打乱\n",
        "    for i in range(0, num_examples, batch_size):\n",
        "        j = torch.LongTensor(indices[i: min(i + batch_size, num_examples)])\n",
        "        # 根据索引返回对应元素，每次取 batch_size 个\n",
        "        yield features.index_select(0, j), labels.index_select(0, j)  # 0 是 dim，返回第 j 行"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gjtCkYYxw-C9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# my_num = 10 \n",
        "# my_num"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JC1MetQn3CRV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# my_indices = list(range(my_num))\n",
        "# my_indices"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hew0ibUV3RXm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# random.shuffle(my_indices)\n",
        "# my_indices"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsE2joz13WRd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# for i in range(0, my_num, 3):\n",
        "#     print(i)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-gQ9Tmt3kCO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# for i in range(0, my_num, 3):\n",
        "#     print(i)\n",
        "#     j = torch.LongTensor(my_indices[i: i + 3])\n",
        "#     print(j)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DvEIXMO4J7p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "outputId": "cf480d28-a4c3-47f2-ebc3-37f2c118d136"
      },
      "source": [
        "batch_size = 10 \n",
        "\n",
        "# 每次返回 batch_size 个样本\n",
        "for X, y in data_iter(batch_size, features, labels):\n",
        "    print(X, '\\n', y)\n",
        "    break"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor([[-1.2592, -0.1401],\n",
            "        [ 0.9659, -0.8890],\n",
            "        [-0.5741,  2.4914],\n",
            "        [-0.5595, -1.2916],\n",
            "        [-0.0661, -0.3741],\n",
            "        [ 0.3544,  3.1017],\n",
            "        [-0.0086, -0.3121],\n",
            "        [-0.6513,  0.0655],\n",
            "        [-0.0551,  0.5879],\n",
            "        [-0.7365,  0.3899]]) \n",
            " tensor([ 2.1649,  9.1704, -5.4148,  7.4768,  5.3336, -5.6348,  5.2580,  2.6728,\n",
            "         2.1010,  1.3987])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bl60yC_84fE0",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.3 初始化模型参数"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-xJNiBk4zeY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w = torch.tensor(np.random.normal(0, 0.01, (num_inputs, 1)), dtype=torch.float32)\n",
        "b = torch.zeros(1, dtype=torch.float32)   # 就是个 0：tensor([0.])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bE9f17jV5ADs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# w.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6mo1EBM5NTJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# b.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4PDpqqw6Udr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5f7a4eef-2cbd-40cd-b88f-e032218dbef1"
      },
      "source": [
        "w.requires_grad_(requires_grad=True)\n",
        "b.requires_grad_(requires_grad=True)"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0.], requires_grad=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaV01woM5W7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# w.requires_grad_?\n",
        "# sets this tensor's :attr:`requires_grad` attribute in-place."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wgpfdd4J6f_f",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.4 定义模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBmVx_v56nyt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def linreg(X, w, b):\n",
        "    return torch.mm(X, w) + b "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yin9auJd6tem",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.5 定义损失函数"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T52BWDsg6ywN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def squared_loss(y_hat, y):\n",
        "    return (y_hat - y.view(y_hat.size())) ** 2 / 2   # 转化成 y_hat 的 shape，对应相减，每一项平方，\n",
        "                                  # 每一项除以 2，返回的 y_hat 的 shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vl1YORC38QWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# my_x = torch.arange(6).view(2, 3)\n",
        "# print(my_x ** 2)\n",
        "# print(my_x ** 2 / 2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8iC6LtqL8qx6",
        "colab_type": "text"
      },
      "source": [
        "### 3.2.6 定义优化算法"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoV_7k-d823w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sgd(params, lr, batch_size):\n",
        "    for param in params:\n",
        "        param.data -= lr * param.grad / batch_size "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "up70gtCP8_Rg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "732c3d6f-3e0e-4eca-ecd9-cbc0a9495fb8"
      },
      "source": [
        "# 读注释，是监督学习的核心思想\n",
        "\n",
        "lr = 0.03 \n",
        "num_epochs = 3 \n",
        "net = linreg \n",
        "loss = squared_loss \n",
        "\n",
        "loss_ls = []\n",
        "for epoch in range(num_epochs):\n",
        "    for X, y in data_iter(batch_size, features, labels):\n",
        "        l = loss(net(X, w, b), y).sum()  # 输入 X 和随机的参数产生输出，用真实的数字和产生结果求 loss\n",
        "        l.backward()              # 本质上就是一个找参数的过程。调整参数，减小 loss。\n",
        "        sgd([w, b], lr, batch_size)     # 最重要的就是这句，是整个算法的灵魂。\n",
        "                            # 之所以能迭代运行，核心在于每次减梯度。\n",
        "                            # 根据loss的梯度，不断迭代 w，b，loss 不断减小，最终参数接近真实值\n",
        "        # 梯度清零\n",
        "        w.grad.data.zero_()\n",
        "        b.grad.data.zero_()\n",
        "        loss_ls.append(l.float().mean().item())\n",
        "    train_l = loss(net(features, w, b), labels) # 用这个 epoch 迭代出来的 w，b 算 loss\n",
        "    print('epoch %d, loss %f' % (epoch + 1, train_l.mean().item()))"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 0.043894\n",
            "epoch 2, loss 0.000171\n",
            "epoch 3, loss 0.000051\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C6B_aCyO-atv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "48dbc4fd-a723-4fc2-e306-874571680654"
      },
      "source": [
        "plt.plot(loss_ls, label='loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoENgcPL-pVV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "938d9e8a-5d01-4c14-cee8-194ac5b00c55"
      },
      "source": [
        "print(true_w, '\\n', w)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2, -3.4] \n",
            " tensor([[ 1.9995],\n",
            "        [-3.3997]], requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cdH2EDBi-vMd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "0a620947-4519-455d-8157-558c832c12f5"
      },
      "source": [
        "print(true_b, '\\n', b)"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4.2 \n",
            " tensor([4.1989], requires_grad=True)\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}